In today's competitive business scenario, most of the companies try to take an edge over others by gathering and analyzing more and more data. The data can be profile information of their customer(s)/potential customer(s)/user(s), counting of the number of customers that has entered their store, and the like. For example, a retail chain owner may like to collect data corresponding to the number of users entered in a particular store and relate the sales with the number of customer entering that store. In addition, the retail chain owner may like to collect the number of people having entered into a particular section of the store. These types of detailed analysis help companies to not only increase their sales but also take strategic marketing decision.
In the last decade, solutions involving radio-communication network in a facility to track mobile devices inside the facility are being utilized to make inferences about the customer(s)/potential customer(s). These solutions are commonly referred to as indoor positioning systems. Some of these indoor positioning systems utilize technical parameters like received signal strength indictor (RSSI) from access points (APs), configuration parameters of sensors, etc. for tracking the mobile devices inside the facility.
However, these technical parameters along with other factors vary with highly dynamic environment, thereby reducing the chances of development of accurate and stable indoor positioning system. For example, radio signals propagate in a complicated manner and are often affected by noise and other sources. This results in unstable and noisy signal strength readings. In addition, the physical characteristics in an indoor setting (for example, walls, furniture, computers, and elevators) affect the Received Signal Strength Indication (RSSI). Further, movement of users within an indoor environment adversely affects the RSSI as human indoor movement tends to be less predictable. Furthermore, accidentally switching off the power plugs of components of the positioning system by clerk, cleaner and the like without notifying the server system do affect the tracking. Moreover, remodeling of the premises (for example, shops) in which the components are attached to movable positions (for example, shelf, racks and the like) also affect the tracking.
In addition, most of the present indoor positioning systems implement a trilateration technique to track the mobile devices. The trilateration is a high level mathematical method of determining an absolute location of a point by measurements of distances using geometry of circles, spheres, triangles and the like. Further, the trilateration finds practical applications in surveying and navigation, including global positioning systems (GPS). However, it does not involve the measurement of angles. In addition, due to a complex and dynamic environment, the trilateration gives only approximate results. Thus, improvements are needed in the trilateration process to improve location accuracy. Another method used presently to count and track position of one or more users includes methods done by door counters. For example, door counter methods include video feed based counters, ultrasonic counters, infrared beam counters, carpet (pressure) sensors and the like. The video feed based counters are advanced stereo vision tracking technologies to count data under a broad set of environmental conditions. However, the door by door counters are unreliable when several people move in and out from the premises at same time.
In light of the above stated discussion, there is a need for a method and system that overcomes the above stated disadvantages.